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Lassonde prof explores optical imaging for heart attack prevention

Jul 5, 2024

Heart disease is a leading cause of death in Canada and the U.S. Understanding the severity of the disease in patients is crucial for preventing complications like heart attacks – and researchers across the globe are racing toward solutions to avoid such devastating outcomes.

Over the past few years, Nima Tabatabaei, an associate professor in the Mechanical Engineering Department at York University’s Lassonde School of Engineering – along with his former PhD student Mohammad Hossein Salimi and Professor Martin Villiger, a researcher at Massachusetts General Hospital and Harvard Medical School – has been working to develop a new intravascular imaging technology to enable early detection and prevention of heart attacks.

Nima Tabatabaei

“When aiming to prevent heart attacks,” Tabatabaei says, “a major point of interest is early detection of unstable atherosclerotic plaques.”

These plaques are made up of fatty substances known as lipids, which can accumulate in different arteries throughout the body.

“If an atherosclerotic plaque ruptures, patients can experience a heart attack,” he says. “However, not all plaques will rupture. A major issue is that cardiologists don’t have a reliable way of distinguishing plaques that are problematic from those that are not.”

Tabatabaei’s new technology, called photo-thermal optical coherence tomography (PT-OCT), can distinguish rupture-prone plaques from stable ones based on their distinct structure and chemistry. An enhanced version of a biomedical imaging technique called optical coherence tomography (OCT), which is primarily used to diagnose eye diseases, PT-OCT uses two lasers as well as principles of interferometry – a measurement method using the interference of superimposed waves to extract information – to capture light scattering and absorption from biological tissues. This generates high-resolution images that show both plaque structure and tissue chemistry, providing an objective tool for assessing the risk of atherosclerotic plaque rupture.

“OCT is one of the most widely used optical imaging methods in clinical settings,” explains Tabatabaei. “Our idea was to include additional lasers to the system at specific wavelengths, allowing for absorption by the chemistry of unstable plaques.”

Although PT-OCT is a promising method for imaging atherosclerotic plaques, it is not currently being used in clinical settings for this purpose.

“Very few labs have access to this technology,” says Tabatabaei, “and it can also be quite slow, complicated and hindered by noise.”

Throughout the past four years, Tabatabaei and his research team have worked to address several fundamental and technological challenges of PT-OCT, while exploring innovative ways to improve this method for the purpose of imaging and characterizing atherosclerotic plaques.

In a recent paper published in the prominent journal Scientific Reports, Tabatabaei explored the use of an artificial intelligence method to improve the signal-to-noise ratio of PT-OCT signals and contrast of PT-OCT images. Neural networks were designed and trained with experimental data, allowing the system to better predict specific structural and chemical features in images. This work also created opportunities to significantly improve imaging speed and efficiency.

Through previous research, the team studied the implications of PT-OCT signals and their relationship with biological tissue, linking optical signals to corresponding chemical structures. They also investigated ways to improve additional qualities like accuracy, working toward practical use in clinical settings.

Detecting and characterizing atherosclerotic plaques, this imaging technology boasts the potential to transform the future of heart attack detection and treatment, ultimately improving patient outcomes. Moving forward, Tabatabaei will continue to apply his expertise in biomedical optics to improve PT-OCT and explore additional applications, contributing to ongoing efforts of combatting heart disease.

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